{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们用机器模拟的时点负荷数据做一个缺失值统计，通过绘图呈现负荷数据缺失的概貌"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x129388c10>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3240x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import missingno as msno\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "# 以下 font.family 设置仅适用于 Mac系统，其它系统请使用对应字体名称\n",
    "matplotlib.rcParams['font.family'] = 'Arial Unicode MS'\n",
    "data = pd.read_csv(\"http://image.cador.cn/data/energy_out.csv\")\n",
    "msno.matrix(data, labels=True,figsize=(45,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现编写Python代码基于K近邻的思路对缺失值进行插补"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "import numpy as np\n",
    "weth = pd.read_csv(\"http://image.cador.cn/data/weather.csv\")\n",
    "# 获取星期数据\n",
    "data['weekday']=[datetime.datetime.strptime(x,'%Y/%m/%d').weekday() for x in data.LOAD_DATE]\n",
    "# 获取月份数据\n",
    "data['month']=[datetime.datetime.strptime(x,'%Y/%m/%d').month for x in data.LOAD_DATE]\n",
    "data['date']=[datetime.datetime.strptime(x,'%Y/%m/%d') for x in data.LOAD_DATE]\n",
    "# 将数据按日期升序排列\n",
    "data = data.sort_values(by='date')\n",
    "# 获取时间趋势数据\n",
    "data['trend'] = range(data.shape[0])\n",
    "# 设置索引并按索引进行关联\n",
    "data=data.set_index('LOAD_DATE')\n",
    "weth=weth.set_index('WETH_DATE')\n",
    "p = data.join(weth)\n",
    "p = p.drop(columns='date')\n",
    "# 声明列表用于存储位置及插补值信息\n",
    "out = list()\n",
    "for index in np.where(p.apply(lambda x:np.sum(np.isnan(x)),axis=1)>0)[0]:\n",
    "    selcol = np.logical_not(np.isnan(p.iloc[index]))\n",
    "    usecol = np.where(selcol)[0]\n",
    "    cols = np.where(~selcol)[0]\n",
    "    for col in cols:\n",
    "        nbs = np.where(p.iloc[:,usecol].apply(lambda x:np.sum(np.isnan(x)),axis=1)==0)[0]\n",
    "        nbs = nbs[nbs != index]\n",
    "        nbs = (list(set(nbs).intersection(set(np.where(np.logical_not(np.isnan(p.iloc[:,col])))[0]))))\n",
    "        t0 = [np.sqrt(np.sum((p.iloc[index,usecol]-p.iloc[x,usecol])**2)) for x in nbs]\n",
    "        t1 = 1/np.array(t0)\n",
    "        t_wts = t1/np.sum(t1)\n",
    "        out.append((index,col,np.sum(p.iloc[nbs,col].values*t_wts)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们可将out打印出来，查看其取值的情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(18, 0, 15.877932471145733),\n",
       " (18, 1, 15.107482604538141),\n",
       " (18, 2, 14.247447130649874),\n",
       " (18, 3, 13.637888316974843),\n",
       " (18, 4, 13.537397118266925),\n",
       " (18, 5, 13.805422103976255),\n",
       " (18, 6, 13.191140292458323),\n",
       " (18, 7, 12.395420895966566),\n",
       " (18, 8, 12.612580328443027),\n",
       " (18, 9, 12.325141881865681)]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "基于out可对数据p进行缺失值插补"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C001</th>\n",
       "      <th>C002</th>\n",
       "      <th>C003</th>\n",
       "      <th>C004</th>\n",
       "      <th>C005</th>\n",
       "      <th>C006</th>\n",
       "      <th>C007</th>\n",
       "      <th>C008</th>\n",
       "      <th>C009</th>\n",
       "      <th>C010</th>\n",
       "      <th>...</th>\n",
       "      <th>C093</th>\n",
       "      <th>C094</th>\n",
       "      <th>C095</th>\n",
       "      <th>C096</th>\n",
       "      <th>weekday</th>\n",
       "      <th>month</th>\n",
       "      <th>trend</th>\n",
       "      <th>MEAN_TMP</th>\n",
       "      <th>MIN_TMP</th>\n",
       "      <th>MAX_TMP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LOAD_DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013/9/1</th>\n",
       "      <td>16.18</td>\n",
       "      <td>10.28</td>\n",
       "      <td>12.84</td>\n",
       "      <td>10.18</td>\n",
       "      <td>11.24</td>\n",
       "      <td>10.90</td>\n",
       "      <td>10.86</td>\n",
       "      <td>10.38</td>\n",
       "      <td>10.26</td>\n",
       "      <td>9.76</td>\n",
       "      <td>...</td>\n",
       "      <td>22.00</td>\n",
       "      <td>18.28</td>\n",
       "      <td>17.92</td>\n",
       "      <td>21.40</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>23.4</td>\n",
       "      <td>31.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013/9/2</th>\n",
       "      <td>18.38</td>\n",
       "      <td>17.76</td>\n",
       "      <td>16.74</td>\n",
       "      <td>14.30</td>\n",
       "      <td>15.46</td>\n",
       "      <td>15.86</td>\n",
       "      <td>14.48</td>\n",
       "      <td>14.00</td>\n",
       "      <td>14.88</td>\n",
       "      <td>14.38</td>\n",
       "      <td>...</td>\n",
       "      <td>27.82</td>\n",
       "      <td>21.56</td>\n",
       "      <td>22.98</td>\n",
       "      <td>24.68</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>28.1</td>\n",
       "      <td>25.5</td>\n",
       "      <td>32.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013/9/3</th>\n",
       "      <td>24.94</td>\n",
       "      <td>19.74</td>\n",
       "      <td>21.86</td>\n",
       "      <td>17.12</td>\n",
       "      <td>21.00</td>\n",
       "      <td>32.38</td>\n",
       "      <td>20.38</td>\n",
       "      <td>16.76</td>\n",
       "      <td>16.74</td>\n",
       "      <td>16.92</td>\n",
       "      <td>...</td>\n",
       "      <td>15.94</td>\n",
       "      <td>16.24</td>\n",
       "      <td>17.66</td>\n",
       "      <td>15.82</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>25.9</td>\n",
       "      <td>24.5</td>\n",
       "      <td>28.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013/9/4</th>\n",
       "      <td>17.06</td>\n",
       "      <td>13.28</td>\n",
       "      <td>13.00</td>\n",
       "      <td>15.12</td>\n",
       "      <td>13.88</td>\n",
       "      <td>13.10</td>\n",
       "      <td>13.38</td>\n",
       "      <td>14.22</td>\n",
       "      <td>13.44</td>\n",
       "      <td>12.54</td>\n",
       "      <td>...</td>\n",
       "      <td>20.36</td>\n",
       "      <td>18.76</td>\n",
       "      <td>19.86</td>\n",
       "      <td>16.86</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>24.7</td>\n",
       "      <td>22.9</td>\n",
       "      <td>26.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013/9/5</th>\n",
       "      <td>12.30</td>\n",
       "      <td>15.22</td>\n",
       "      <td>11.18</td>\n",
       "      <td>11.08</td>\n",
       "      <td>9.68</td>\n",
       "      <td>12.72</td>\n",
       "      <td>11.28</td>\n",
       "      <td>10.52</td>\n",
       "      <td>10.16</td>\n",
       "      <td>9.94</td>\n",
       "      <td>...</td>\n",
       "      <td>19.12</td>\n",
       "      <td>22.28</td>\n",
       "      <td>16.86</td>\n",
       "      <td>11.14</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>24.5</td>\n",
       "      <td>22.8</td>\n",
       "      <td>26.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 102 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            C001   C002   C003   C004   C005   C006   C007   C008   C009  \\\n",
       "LOAD_DATE                                                                  \n",
       "2013/9/1   16.18  10.28  12.84  10.18  11.24  10.90  10.86  10.38  10.26   \n",
       "2013/9/2   18.38  17.76  16.74  14.30  15.46  15.86  14.48  14.00  14.88   \n",
       "2013/9/3   24.94  19.74  21.86  17.12  21.00  32.38  20.38  16.76  16.74   \n",
       "2013/9/4   17.06  13.28  13.00  15.12  13.88  13.10  13.38  14.22  13.44   \n",
       "2013/9/5   12.30  15.22  11.18  11.08   9.68  12.72  11.28  10.52  10.16   \n",
       "\n",
       "            C010  ...   C093   C094   C095   C096  weekday  month  trend  \\\n",
       "LOAD_DATE         ...                                                      \n",
       "2013/9/1    9.76  ...  22.00  18.28  17.92  21.40        6      9      0   \n",
       "2013/9/2   14.38  ...  27.82  21.56  22.98  24.68        0      9      1   \n",
       "2013/9/3   16.92  ...  15.94  16.24  17.66  15.82        1      9      2   \n",
       "2013/9/4   12.54  ...  20.36  18.76  19.86  16.86        2      9      3   \n",
       "2013/9/5    9.94  ...  19.12  22.28  16.86  11.14        3      9      4   \n",
       "\n",
       "           MEAN_TMP  MIN_TMP  MAX_TMP  \n",
       "LOAD_DATE                              \n",
       "2013/9/1       26.5     23.4     31.2  \n",
       "2013/9/2       28.1     25.5     32.2  \n",
       "2013/9/3       25.9     24.5     28.7  \n",
       "2013/9/4       24.7     22.9     26.1  \n",
       "2013/9/5       24.5     22.8     26.2  \n",
       "\n",
       "[5 rows x 102 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for v in out:\n",
    "    p.iloc[v[0],v[1]]=v[2]\n",
    "    \n",
    "p.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12879af10>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3240x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(p, labels=True,figsize=(45,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "p.to_csv('data/p.csv', index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
